Retrieval of cloud optical depth through radiative transfer and remote sensing: from 1D to 3D approach.

The radiative closure methodologies to obtain Cloud Optical Depth (COD) from Remote Sensing techniques have traditionally relied on one-dimensional (1D) assumptions. These assumptions might be far away from the radiation transport over a realistic three-dimensional (3D) atmosphere, especially in cloudy conditions, as the natural inhomogeneities of clouds are not conveniently represented and treated in 1D models. The differences between the 1D and 3D approaches manifests in the 3D effects: a) the plane-parallel albedo bias and, b) the horizontal transport effect. The plane-parallel albedo bias is usually addressed by means of the Independent Pixel approximation (IPA), that considers each pixel radiatively independent from the others. Nevertheless, the IPA neglects the horizontal transport, entailing bias in the retrievals. In this work, we use the advantages of 3D radiative transfer (RT) to analyze COD and parameterize the 3D biases in terms of the plane-parallel approach. Detailed 3D RT simulations using MYSTIC are performed over two Highly Resolved Large Eddy Simulations cloud fields of known optical thickness. The output radiance is analyzed by a 1D IPA inversion retrieval based on a radiative closure to obtain the COD. The comparison between the retrieved COD fields for diverse illumination conditions and the real COD allow us to study the 3D effects separately and evaluate the retrieval. Our results show radiation enhancement in cloud edges depending on solar, viewing and cloud geometries, that induces a COD underestimation. The 1D approach works well for overcast conditions and underestimates the COD in broken clouds scenarios.